AI-Powered Competitive Advertising: Multimodal Personalization for Digital Markets
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This paper studies an agentic, multimodal AI framework for hyper-personalized, real-time ad targeting in competitive digital markets across B2B and B2C contexts, using foundation models with retrieval-augmented generation, multimodal reasoning, and persona-based adaptation. The key reported finding is that the approach improves engagement and optimizes Return on Ad Spend (ROAS), with validation conducted via both real-world and synthetic market simulations and emphasizing adaptive, privacy-compliant, and scalable operation. A major caveat explicitly noted is that the work is a preprint and has not been peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
Abstract The rapid evolution of AI-driven advertising has transformed how businesses engage with consumers in competitive markets. This study presents an agentic, multimodal framework that leverages foundation models to enable hyper-personalized, real-time ad targeting across B2B and B2C sectors. By integrating retrieval-augmented generation (RAG), multimodal reasoning, and persona-based adaptation, our approach enhances engagement while optimizing Return on Ad Spend (ROAS). Experimental validation through real-world and synthetic market simulations demonstrates the framework’s effectiveness in adaptive, privacy-compliant, and scalable advertising strategies.
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AI-Powered Competitive Advertising: Multimodal Personalization for Digital Markets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Powered Competitive Advertising: Multimodal Personalization for Digital Markets TEJAS VARMA VENKATASWAMY This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8507977/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid evolution of AI-driven advertising has transformed how businesses engage with consumers in competitive markets. This study presents an agentic, multimodal framework that leverages foundation models to enable hyper-personalized, real-time ad targeting across B2B and B2C sectors. By integrating retrieval-augmented generation (RAG), multimodal reasoning, and persona-based adaptation, our approach enhances engagement while optimizing Return on Ad Spend (ROAS). Experimental validation through real-world and synthetic market simulations demonstrates the framework’s effectiveness in adaptive, privacy-compliant, and scalable advertising strategies. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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